The prime focus on the deliberations related to Industry 5.0 lies in creativity, efficiency and resilience for adapting various organic components such as accurate decisions, customized demands in consumer electronics with promising/sustainable solutions. This transformation and paradigm shift inside the Industry 5.0 frame can be comprehensively understood by integrating the factors related to human values with the cutting-edge technologies of consumer electronics for compatibility with the issue of sustainable development. The present work proposes an integrated approach for extensive study with a set of criteria and possible Industry 5.0 enablers in consumer electronics using a novel decision-making approach. The approach comprises of T-spherical fuzzy information containment/processing through an analytic hierarchy process (AHP) and then utilizing the weighted aggregated sum product assessment (WASPAS) sequentially to obtain the necessary weights of selected criteria. Further, the obtained weights from AHP would be used in WASPAS for the computational assessment while finding the prioritization/ranking of industry 5.0 enablers. The results with the sensitivity analysis validate the robustness-cum-resilience of the proposed integrated approach with standard managerial implications and the findings take care of cognitive in the human socio-technical environment. The regulated adoption of Industry 5.0 enablers would help consumer electronics manufacturers in finding optimized solutions.

Many cutting-edge technologies, such as MIMO, cognitive radio, multi-carrier modulation, and network coding, have been proposed for wireless communication to satisfy needs for a higher data rate in the upcoming time, leading to improved quality of service (QoS) regardless of the weather. Orthogonal and non-orthogonal multiple access techniques are two categories into which multiple access technologies can be subdivided. Large networking with effective implementation of wireless devices is supported by non-orthogonal multiple access techniques. Massive NOMA has been implemented to advance access efficiency by permitting several users to share a similar spectrum. Because of the robust co-channel interference between mobile users presented by NOMA, it offers important tasks for system model and resources management. In this study, two additional sets of demanding codes are explored. Multi-user shared access methods and expanded multi-user shared access (EMUSA) methods are both employed. In the MUSA technique, an algorithm is used for the allocation of resources to achieve minimum intercorrelation to the maximum extent in 5G networks. A novel idea proposed in this paper is to create complex codes starting from PN codes (i.e., ePN), thereby achieving promising results in the overall system performance. The first part of this paper describes the fundamental principles of MUSA, and in the next part the main idea of the proposed technique will be studied in detail. Using Monte-Carlo MATLAB simulation, the performance of the suggested approach is assessed in terms of BER vs. SNR. The efficiency of the proposed approach is evaluated in various settings, and the outcomes are contrasted with those of the traditional CDMA technique, using parameters, such as the number of active users and antennas at the receiver.

Urban sensing plays a significant role in improving resource management, citizen engagement, environmental monitoring, urban planning, safety and social equality. GPS (Global Positioning System) is a crucial part in urban sensing as it provides accurate location tracking, real-time data collection, location-based services, mobility and transportation solutions, intelligent urban planning, and disaster management. However, there are various challenges associated with accurately estimating positions in urban environments due to various factors such as signal obstruction, urban canyons, multipath interference, noise and signal degradation and differential GPS limitations. In the context of GPS based urban sensing applications; the use of a navigation algorithm plays a critical role in extracting reliable data from corrupt sources, which can significantly impact inference performance for various signal processing applications. However, modeling all error sources that affect data quality can significantly increase system complexity, leading to challenges in terms of hardware and computation. To address this challenge, this paper proposes a novel particle filter-based algorithm, called the Double Resampling-based Least-Squares Particle Filter (DR-LPF), designed specifically for estimating the position of a GPS receiver without the need to model all error sources. By integrating current measurements into the particle before resampling through the least squares method, the DR-LPF allows the double-resampled particles to move towards high likelihood regions, leading to improved estimation accuracy, reduced computation time, and reduced computational load. The application of the proposed DR-LPF algorithm finds wide applications in urban sensing environments where data quality can be affected by multiple error sources. By reducing the computational load and improving estimation accuracy, the proposed DR-LPF algorithm can provide valuable insights into the movement and behavior of individuals and objects within an urban environment, enabling a wide range of smart city applications, such as traffic monitoring, environmental sensing, and crowd management.